def trigger_reflection_mode(self):
        """
        Implementation of Option 1: The 'Sleep' Protocol.
        
        Philosophy:
        "Sleep determines our emotion and thinking."
        We function by reducing noise and clearing space, NOT by generating illusions.
        """
        print(">>> [System] Brain fog detected (Low T-Score). Entering Deep Sleep Mode...")
        
        with torch.no_grad(): # No new learning, just organizing
            for name, param in self.compression_layer.named_parameters():
                if 'weight' in name:
                    # 1. The "Detox" (Pruning / 修剪噪音)
                    # Identify weak connections that are just "noise" (below a threshold)
                    # These are the "trivial worries" cluttering the mind.
                    threshold = torch.std(param) * 0.1
                    mask = torch.abs(param) > threshold
                    
                    # Apply the mask: Zero out the noise.
                    # This creates "Space" (Sparsity) -> Restoring "Surplus Energy"
                    param.data.mul_(mask)
                    
                    # 2. The "Calm Down" (Weight Decay / 情绪平复)
                    # Gently shrink the remaining strong weights.
                    # Prevents obsession (overfitting) and arrogance (too large weights).
                    param.data.mul_(0.99) 
                    
                    # 3. The "Refresh" (Minor Noise Injection / 激活生机)
                    # Add tiny gaussian noise to shake the system out of local rigid spots.
                    # Like stretching after waking up.
                    noise = torch.randn_like(param) * 0.001
                    param.data.add_(noise)
                    
        # Reset the Wisdom Metric after sleep
        self.last_T_score = 1.0 
        print(">>> [System] Woke up refreshed. Surplus Energy restored.")